YaBeSH Engineering and Technology Library

    • Journals
    • PaperQuest
    • YSE Standards
    • YaBeSH
    • Login
    View Item 
    •   YE&T Library
    • ASME
    • Journal of Computing and Information Science in Engineering
    • View Item
    •   YE&T Library
    • ASME
    • Journal of Computing and Information Science in Engineering
    • View Item
    • All Fields
    • Source Title
    • Year
    • Publisher
    • Title
    • Subject
    • Author
    • DOI
    • ISBN
    Advanced Search
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Archive

    A Coarse-Grained Regularization Method of Convolutional Kernel for Molten Pool Defect Identification

    Source: Journal of Computing and Information Science in Engineering:;2020:;volume( 020 ):;issue: 002
    Author:
    Liu, Tianyuan
    ,
    Bao, Jinsong
    ,
    Wang, Junliang
    ,
    Zhang, Yiming
    DOI: 10.1115/1.4045294
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: Machine vision has a wide range of applications in the field of welding. The rise of convolutional neural network (CNN) provides a new way to extract visual features of welding. Due to the limitation of the small size of our molten pool dataset, the regularization of the CNN model is necessary to prevent overfitting. We propose a coarse-grained regularization method for convolution kernels (CGRCKs), which is designed to maximize the difference between convolution kernels in the same layer. The algorithm performance was tested on our self-made dataset and other public datasets. The results show that the CGRCK method can extract multi-faceted features. Compared with L1 or L2 regularization, the proposed method works great on CNNs and introduces little overhead cost to the training.
    • Download: (692.6Kb)
    • Show Full MetaData Hide Full MetaData
    • Get RIS
    • Item Order
    • Go To Publisher
    • Price: 5000 Rial
    • Statistics

      A Coarse-Grained Regularization Method of Convolutional Kernel for Molten Pool Defect Identification

    URI
    http://yetl.yabesh.ir/yetl1/handle/yetl/4273578
    Collections
    • Journal of Computing and Information Science in Engineering

    Show full item record

    contributor authorLiu, Tianyuan
    contributor authorBao, Jinsong
    contributor authorWang, Junliang
    contributor authorZhang, Yiming
    date accessioned2022-02-04T14:23:52Z
    date available2022-02-04T14:23:52Z
    date copyright2020/01/03/
    date issued2020
    identifier issn1530-9827
    identifier otherjcise_20_2_021005.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4273578
    description abstractMachine vision has a wide range of applications in the field of welding. The rise of convolutional neural network (CNN) provides a new way to extract visual features of welding. Due to the limitation of the small size of our molten pool dataset, the regularization of the CNN model is necessary to prevent overfitting. We propose a coarse-grained regularization method for convolution kernels (CGRCKs), which is designed to maximize the difference between convolution kernels in the same layer. The algorithm performance was tested on our self-made dataset and other public datasets. The results show that the CGRCK method can extract multi-faceted features. Compared with L1 or L2 regularization, the proposed method works great on CNNs and introduces little overhead cost to the training.
    publisherThe American Society of Mechanical Engineers (ASME)
    titleA Coarse-Grained Regularization Method of Convolutional Kernel for Molten Pool Defect Identification
    typeJournal Paper
    journal volume20
    journal issue2
    journal titleJournal of Computing and Information Science in Engineering
    identifier doi10.1115/1.4045294
    page21005
    treeJournal of Computing and Information Science in Engineering:;2020:;volume( 020 ):;issue: 002
    contenttypeFulltext
    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
    yabeshDSpacePersian
     
    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
    yabeshDSpacePersian